VIBRANT Trial - Primary Analyses

Author

Laura Symul (UCLouvain), Laura Vermeren (UCLouvain), Susan Holmes (Stanford University)

Published

July 11, 2025

Code 
library(tidyverse)
library(magrittr)
library(gt)
library(patchwork)
library(SummarizedExperiment)
library(tidySummarizedExperiment)
library(brms)
library(gtsummary)
library(labelled)
library(scales)
# library(gridExtra)
library(ggpubr)
# library(mia) # BiocManager::install("mia")

theme_set(theme_light())
tmp <- fs::dir_map("R", source)
rm(tmp)
Code 
tmp <- fs::dir_map("../VIBRANT-99-utils/R/", source)
rm(tmp)

This document details the analyses and provides results for the primary analyses of the VIBRANT trial.

Trial data

The data used for these analyses are stored in an R Bioconductor MultiAssayExperiment object which contains the QCed and pre-processed data. The code and description of how raw data have been processed and QCed is available on the VIBRANT-0-QC-and-preprocessing GitHub repository (Still private at the moment, but will be made public upon publication, and anyone wishing to have access can email Laura Symul).

Code 
mae_file <- 
  fs::dir_ls(str_c(data_dir(), "04 unblinded MAEs/"), regexp = "mae_full_.*\\.rds$") |> 
  sort(decreasing = TRUE) |>
  magrittr::extract(1)

cat(str_c("Loading the RDS file containing the unblinded VIBRANT MAE: /", mae_file |> str_remove(data_dir()), "\n"))
Loading the RDS file containing the unblinded VIBRANT MAE: /04 unblinded MAEs/mae_full_20250624.rds
Code 
mae <- readRDS(mae_file)

rm(mae_file)

Demographics (Table 1 & suppl. tables by sites)

Summary table

The demographics table is built as follow.

We first create a table1_data data.frame from the participant_crfs_merged table stored in the @metadata slot of the mae.

This table contains almost all variables needed to create the demographics table (i.e., site, age, race, ethn, cut_size_meals, eat_less, hungry_did_not_eat, sexual_partners_lifetime, sexual_partners_past_month, contraception_method).

In addition, the mae@colData contains the participants’ arm and study population flags (here we keep the mITT population).

From the variables listed above, we create the variable food_insecurity using the three variables cut_size_meals, eat_less and hungry_did_not_eat: if a participant answered “Yes” to any of the corresponding questions, then she is considered to be affected by food insecurity.

Code 
table1_data <- 
  metadata(mae)$participant_crfs_merged |> 
  dplyr::left_join(
    colData(mae) |> as_tibble() |> select(pid, site, arm, ITT, mITT, PP) |> distinct(), 
    by = join_by(pid, site)
    )  |> 
  filter(mITT) |> 
  select(pid, site, arm, age, race, ethn, cut_size_meals, eat_less, hungry_did_not_eat, sexual_partners_lifetime, sexual_partners_past_month, contraception_method) |> 
  mutate(
    food_insecurity = ifelse(
      cut_size_meals == "Yes" | eat_less == "Yes" | hungry_did_not_eat == "Yes",
      "Yes",
      "No"
    ),
    food_insecurity = factor(food_insecurity, levels = c("No", "Yes"))
  ) |> 
  select(-c(eat_less, cut_size_meals, hungry_did_not_eat))
Code 
table1_data |> 
  ggplot(aes(x = food_insecurity)) +
  geom_bar() +
  labs(x= "Food insecurity", y = "Count")+
  theme_classic()

In addition to these variables, we also include the participants’ partner’s gender.

Participants are asked about their partner’s gender at all weekly visits (1000, 1100, 1200, 1300, 1400, 1500, 1700, 1900, 2120) using CRF 19.

Answers to that question can be found in the past_week_sex_partner column of the mae@metadata$visits_crfs_merged table. Participants’ answer throughout the weekly visits will be summarized into one of these 4 categories:

  • No sex
  • Only male
  • Only female
  • Both

This variable is added to the table1_data table.

Code 
gender_sexual_partner <- 
  metadata(mae)$visits_crfs_merged |>
  select(pid, past_week_sex_partner) |>
  filter(!is.na(past_week_sex_partner)) |>
  group_by(pid) |>
  summarise(
    gender_sexual_partner = 
      case_when(
        all(past_week_sex_partner == "No sex in the past week") ~ "No sex",
        any(past_week_sex_partner %in% c("A single male partner", "Multiple male partners")) &
          !any(past_week_sex_partner %in% c("A single female partner", "Multiple female partners")) ~ "Only man",
        any(past_week_sex_partner %in% c("A single female partner", "Multiple female partners")) &
          !any(past_week_sex_partner %in% c("A single male partner", "Multiple male partners")) ~ "Only female",
        any(past_week_sex_partner %in% c("A single male partner", "Multiple male partners")) &
          any(past_week_sex_partner %in% c("A single female partner", "Multiple female partners")) ~ "Both",
        TRUE ~ NA_character_
      ),
    .groups = "drop"
  )

table1_data <- 
  table1_data |> 
  left_join(gender_sexual_partner, by = "pid")

We also re-code the race variable as follows:

  • “Asian or South Asian” is re-coded to “Asian” (for brevity)
  • “Black, African American, or African” and “African” are re-coded to “Black/African” (for brevity)
  • “Coloured”, “White”, “Other”, and “Prefered not to answer” are left as is.
Note

@Disebo & Caroline: do we go for British or American English? (Colored vs Coloured)?

Code 
table1_data <-
  table1_data |>
  
  mutate(
    across(where(is.character), as.factor),
    
    race = fct_recode(
      race,
      "Asian" = "Asian or South Asian",
      "Black/African" = "Black, African American, or African",
      "Black/African" = "African",
      "Coloured" = "Coloured",
      "White" = "White",
      "Other" = "Other",
      "Prefer not to answer" = "Prefer not to answer"
    ),
    
    race = fct_relevel(race, "Asian", "Black/African", "Coloured", "White", "Other", "Prefer not to answer"),
    race = race |> fct_drop(),
    arm = arm |> fct_drop()
  ) |>
  
  set_variable_labels(
    food_insecurity = "Report food insecurity in past 12 months ",
    sexual_partners_past_month = "Number of partners in past month",
    sexual_partners_lifetime = "Number of partners in lifetime",
    ethn = "Ethnicity", 
    contraception_method = "Contraception", 
    gender_sexual_partner = "Gender of sexual partners"
  )
Warning

@Laura Vermeren: I changed the summary statistics from mean to median for the continuous variables (for number of past sexual partner, the mean was artificially high in the arm with the 90 partner MGH participant). Would you be able to check that the tests selected by the gt_summary package are appropriate for the data we have? They should be, but I’d love if we could double-check :)

Table 1 (Population characteristics by arm)

Code 
table1_data |> 
  select(-pid) |>
  rename_with(~ str_to_title(.x)) |>  
  tbl_summary(
    by = Arm,
    type =
      list(
        Sexual_partners_past_month ~ "continuous",
        Sexual_partners_lifetime ~ "continuous"
      ),
    statistic = list(
      all_continuous() ~ "{median} ({min}-{max})",
      all_categorical() ~ "{n} ({p}%)"
    )
  )  |> 
  add_p(include = -Site)
Characteristic Pl
N = 191		 LC-106-7
N = 211		 LC-106-3
N = 151		 LC-106-o
N = 151		 LC-115
N = 201		 p-value2
Site





    CAP 14 (74%) 14 (67%) 14 (93%) 14 (93%) 14 (70%)
    MGH 5 (26%) 7 (33%) 1 (6.7%) 1 (6.7%) 6 (30%)
Age 29 (20-38) 29 (18-40) 26 (21-34) 25 (19-35) 30 (20-40) 0.5
Race




0.5
    Asian 1 (5.3%) 0 (0%) 1 (6.7%) 0 (0%) 0 (0%)
    Black/African 14 (74%) 18 (86%) 14 (93%) 15 (100%) 18 (90%)
    White 2 (11%) 3 (14%) 0 (0%) 0 (0%) 1 (5.0%)
    Other 1 (5.3%) 0 (0%) 0 (0%) 0 (0%) 1 (5.0%)
    Prefer not to answer 1 (5.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Ethnicity




0.8
    Not hispanic/latino/spanish 3 (60%) 6 (86%) 1 (100%) 1 (100%) 4 (67%)
    hispanic/latino/spanish 2 (40%) 1 (14%) 0 (0%) 0 (0%) 2 (33%)
    Unknown 14 14 14 14 14
Number of partners in lifetime 5 (1-10) 5 (1-90) 4 (1-10) 4 (1-20) 5 (1-10) 0.8
Number of partners in past month 1 (1-2) 1 (0-2) 1 (0-2) 1 (0-3) 1 (0-2) 0.4
Contraception




>0.9
    Continuous combined oral contraceptive pills (skip placebo) 3 (16%) 5 (24%) 1 (6.7%) 1 (6.7%) 3 (15%)
    Contraceptive vaginal ring 0 (0%) 1 (4.8%) 0 (0%) 0 (0%) 0 (0%)
    Cyclic combined oral contraceptive pills 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (5.0%)
    Depo provera 11 (58%) 10 (48%) 12 (80%) 11 (73%) 11 (55%)
    Hormonal implant 1 (5.3%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Hormone containing IUD 2 (11%) 2 (9.5%) 0 (0%) 0 (0%) 2 (10%)
    Net-EN 2 (11%) 2 (9.5%) 2 (13%) 3 (20%) 2 (10%)
    Other 0 (0%) 1 (4.8%) 0 (0%) 0 (0%) 1 (5.0%)
Report food insecurity in past 12 months 7 (37%) 7 (33%) 6 (40%) 5 (33%) 6 (30%) >0.9
Gender of sexual partners




0.8
    No sex 1 (5.3%) 3 (14%) 1 (6.7%) 1 (6.7%) 3 (15%)
    Only man 18 (95%) 18 (86%) 14 (93%) 14 (93%) 17 (85%)
1 n (%); Median (Min-Max)
2 Kruskal-Wallis rank sum test; Fisher’s exact test; Pearson’s Chi-squared test

Study population characteristics appear balanced across arms.

Population characteristics by site

Code 
table1_data |> 
  select(arm, age, race, site, food_insecurity, contraception_method, sexual_partners_past_month, sexual_partners_lifetime) |>
  rename_with(~ str_to_title(.x)) |>  
  tbl_summary(
    by = Site,
    type =
      list(
        Sexual_partners_past_month ~ "continuous",
        Sexual_partners_lifetime ~ "continuous"
      ),
    statistic = list(
      all_continuous() ~ "{median} ({min}-{max})",
      all_categorical() ~ "{n} ({p}%)"
    )
  )  |> 
  add_p()
Characteristic CAP
N = 701		 MGH
N = 201		 p-value2
Arm

0.2
    Pl 14 (20%) 5 (25%)
    LC-106-7 14 (20%) 7 (35%)
    LC-106-3 14 (20%) 1 (5.0%)
    LC-106-o 14 (20%) 1 (5.0%)
    LC-115 14 (20%) 6 (30%)
Age 26 (18-37) 33 (22-40) <0.001
Race

<0.001
    Asian 0 (0%) 2 (10%)
    Black/African 70 (100%) 9 (45%)
    White 0 (0%) 6 (30%)
    Other 0 (0%) 2 (10%)
    Prefer not to answer 0 (0%) 1 (5.0%)
Report food insecurity in past 12 months 28 (40%) 3 (15%) 0.038
Contraception

<0.001
    Continuous combined oral contraceptive pills (skip placebo) 5 (7.1%) 8 (40%)
    Contraceptive vaginal ring 0 (0%) 1 (5.0%)
    Cyclic combined oral contraceptive pills 0 (0%) 1 (5.0%)
    Depo provera 54 (77%) 1 (5.0%)
    Hormonal implant 0 (0%) 1 (5.0%)
    Hormone containing IUD 0 (0%) 6 (30%)
    Net-EN 11 (16%) 0 (0%)
    Other 0 (0%) 2 (10%)
Number of partners in past month 1 (0-3) 1 (0-2) 0.4
Number of partners in lifetime 4 (1-20) 10 (4-90) <0.001
1 n (%); Median (Min-Max)
2 Fisher’s exact test; Wilcoxon rank sum test; Pearson’s Chi-squared test

There are, unsurprisingly, striking differences in study population characteristics by site.

Population characteristics by arm, split by site

Code 
table1_data |> 
  filter(site == "CAP") |> 
  select(-c(pid, site)) |>
  rename_with(~ str_to_title(.x)) |>  
  tbl_summary(
    by = Arm,
    type =
      list(
        Sexual_partners_past_month ~ "continuous",
        Sexual_partners_lifetime ~ "continuous"
      ),
    statistic = list(
      all_continuous() ~ "{median} ({min}-{max})",
      all_categorical() ~ "{n} ({p}%)"
    )
  ) |> 
  add_p()|>
  modify_caption("**Site = CAP**")
Site = CAP
Characteristic Pl
N = 141		 LC-106-7
N = 141		 LC-106-3
N = 141		 LC-106-o
N = 141		 LC-115
N = 141		 p-value2
Age 26 (20-36) 26 (18-36) 26 (21-34) 25 (19-35) 25 (20-37) >0.9
Race




>0.9
    Asian 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Black/African 14 (100%) 14 (100%) 14 (100%) 14 (100%) 14 (100%)
    White 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Other 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Prefer not to answer 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Ethnicity





    Not hispanic/latino/spanish 0 (NA%) 0 (NA%) 0 (NA%) 0 (NA%) 0 (NA%)
    hispanic/latino/spanish 0 (NA%) 0 (NA%) 0 (NA%) 0 (NA%) 0 (NA%)
    Unknown 14 14 14 14 14
Number of partners in lifetime 4 (1-10) 3 (1-7) 4 (1-10) 4 (1-20) 4 (1-9) 0.8
Number of partners in past month 1 (1-2) 1 (1-2) 1 (0-2) 1 (0-3) 1 (1-2) 0.2
Contraception




0.9
    Continuous combined oral contraceptive pills (skip placebo) 1 (7.1%) 2 (14%) 0 (0%) 0 (0%) 2 (14%)
    Contraceptive vaginal ring 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Cyclic combined oral contraceptive pills 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Depo provera 11 (79%) 10 (71%) 12 (86%) 11 (79%) 10 (71%)
    Hormonal implant 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Hormone containing IUD 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Net-EN 2 (14%) 2 (14%) 2 (14%) 3 (21%) 2 (14%)
    Other 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Report food insecurity in past 12 months 7 (50%) 5 (36%) 6 (43%) 5 (36%) 5 (36%) >0.9
Gender of sexual partners




>0.9
    No sex 1 (7.1%) 2 (14%) 1 (7.1%) 1 (7.1%) 2 (14%)
    Only man 13 (93%) 12 (86%) 13 (93%) 13 (93%) 12 (86%)
1 Median (Min-Max); n (%)
2 Kruskal-Wallis rank sum test; Fisher’s exact test; Pearson’s Chi-squared test
Code 
table1_data |> 
  filter(site == "MGH") |> 
  select(-c(pid, site)) |>
  rename_with(~ str_to_title(.x)) |>  
  tbl_summary(
    by = Arm,
    type =
      list(
        Sexual_partners_past_month ~ "continuous",
        Sexual_partners_lifetime ~ "continuous"
      ),
    statistic = list(
      all_continuous() ~ "{median} ({min}-{max})",
      all_categorical() ~ "{n} ({p}%)"
    )
  ) |> 
  add_p()|>
  modify_caption("**Site = MGH**")
Site = MGH
Characteristic Pl
N = 51		 LC-106-7
N = 71		 LC-106-3
N = 11		 LC-106-o
N = 11		 LC-115
N = 61		 p-value2
Age 32 (26-38) 33 (24-40) 25 (25-25) 35 (35-35) 33 (22-40) 0.7
Race




0.13
    Asian 1 (20%) 0 (0%) 1 (100%) 0 (0%) 0 (0%)
    Black/African 0 (0%) 4 (57%) 0 (0%) 1 (100%) 4 (67%)
    White 2 (40%) 3 (43%) 0 (0%) 0 (0%) 1 (17%)
    Other 1 (20%) 0 (0%) 0 (0%) 0 (0%) 1 (17%)
    Prefer not to answer 1 (20%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Ethnicity




0.8
    Not hispanic/latino/spanish 3 (60%) 6 (86%) 1 (100%) 1 (100%) 4 (67%)
    hispanic/latino/spanish 2 (40%) 1 (14%) 0 (0%) 0 (0%) 2 (33%)
Number of partners in lifetime 9 (7-10) 15 (6-90) 7 (7-7) 10 (10-10) 6 (4-10) 0.059
Number of partners in past month 1 (1-2) 1 (0-2) 1 (1-1) 1 (1-1) 1 (0-2) >0.9
Contraception




>0.9
    Continuous combined oral contraceptive pills (skip placebo) 2 (40%) 3 (43%) 1 (100%) 1 (100%) 1 (17%)
    Contraceptive vaginal ring 0 (0%) 1 (14%) 0 (0%) 0 (0%) 0 (0%)
    Cyclic combined oral contraceptive pills 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (17%)
    Depo provera 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (17%)
    Hormonal implant 1 (20%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Hormone containing IUD 2 (40%) 2 (29%) 0 (0%) 0 (0%) 2 (33%)
    Net-EN 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
    Other 0 (0%) 1 (14%) 0 (0%) 0 (0%) 1 (17%)
Report food insecurity in past 12 months 0 (0%) 2 (29%) 0 (0%) 0 (0%) 1 (17%) 0.8
Gender of sexual partners




>0.9
    No sex 0 (0%) 1 (14%) 0 (0%) 0 (0%) 1 (17%)
    Only man 5 (100%) 6 (86%) 1 (100%) 1 (100%) 5 (83%)
1 Median (Min-Max); n (%)
2 Kruskal-Wallis rank sum test; Fisher’s exact test

Study population characteristics appear balanced across arms at the CAPRISA site. Given the low number of participants at the MGH site in two of the five arms, we create an additional table excluding these two arms.

Code 
table1_data |> 
  filter(site == "MGH", !(arm %in% c("LC-106-o", "LC-106-3"))) |> 
  mutate(arm = arm |> fct_drop()) |> 
  select(-c(pid, site)) |>
  rename_with(~ str_to_title(.x)) |>  
  tbl_summary(
    by = Arm,
    type =
      list(
        Sexual_partners_past_month ~ "continuous",
        Sexual_partners_lifetime ~ "continuous"
      ),
    statistic = list(
      all_continuous() ~ "{median} ({min}-{max})",
      all_categorical() ~ "{n} ({p}%)"
    )
  ) |> 
  add_p()|>
  modify_caption("**Site = MGH**")
Site = MGH
Characteristic Pl
N = 51		 LC-106-7
N = 71		 LC-115
N = 61		 p-value2
Age 32 (26-38) 33 (24-40) 33 (22-40) >0.9
Race


0.14
    Asian 1 (20%) 0 (0%) 0 (0%)
    Black/African 0 (0%) 4 (57%) 4 (67%)
    White 2 (40%) 3 (43%) 1 (17%)
    Other 1 (20%) 0 (0%) 1 (17%)
    Prefer not to answer 1 (20%) 0 (0%) 0 (0%)
Ethnicity


0.7
    Not hispanic/latino/spanish 3 (60%) 6 (86%) 4 (67%)
    hispanic/latino/spanish 2 (40%) 1 (14%) 2 (33%)
Number of partners in lifetime 9 (7-10) 15 (6-90) 6 (4-10) 0.017
Number of partners in past month 1 (1-2) 1 (0-2) 1 (0-2) 0.8
Contraception


>0.9
    Continuous combined oral contraceptive pills (skip placebo) 2 (40%) 3 (43%) 1 (17%)
    Contraceptive vaginal ring 0 (0%) 1 (14%) 0 (0%)
    Cyclic combined oral contraceptive pills 0 (0%) 0 (0%) 1 (17%)
    Depo provera 0 (0%) 0 (0%) 1 (17%)
    Hormonal implant 1 (20%) 0 (0%) 0 (0%)
    Hormone containing IUD 2 (40%) 2 (29%) 2 (33%)
    Net-EN 0 (0%) 0 (0%) 0 (0%)
    Other 0 (0%) 1 (14%) 1 (17%)
Report food insecurity in past 12 months 0 (0%) 2 (29%) 1 (17%) 0.7
Gender of sexual partners


>0.9
    No sex 0 (0%) 1 (14%) 1 (17%)
    Only man 5 (100%) 6 (86%) 5 (83%)
1 Median (Min-Max); n (%)
2 Kruskal-Wallis rank sum test; Fisher’s exact test

Besides the number of lifetime sexual partners, arms appear balanced across arm at MGH.

Primary outcomes (Table 2)

LBP colonization by week 5

The primary outcome is defined as “Colonization by any of the LBP strains after product administration by week 5”, and colonization by any of the LBP strain is defined as a relative abundance of 5% by any single strain or 10% total relative abundance of the LBP strains. The relative abundance of LBP strain is estimated using metagenomics (taxonomic composition estimated using VIRGO2 and strain proportion of total L. crispatus is estimated using kSanity), and includes samples from weekly visits 1200 to 1500 (week 2 (post-INT) to week 5 (3 weeks post-INT)).

The computation itself has been done at the QC/Pre-processing stage (see the VIBRANT-0-QC-and-preprocessing GitHub repository).

Code 
col_by_week5 <- 
  mae@colData |> as_tibble() |> 
  select(pid, site, randomized, arm, ITT, mITT, PP) |> 
  distinct() |> 
  filter(randomized, mITT) |> 
  left_join(
    mae[["col_LBP_mg"]] |> 
      as_tibble() |> 
      dplyr::left_join(
        mae@colData |> as_tibble() |> select(uid, pid, visit_code),
        by = join_by(.sample == uid)
      ) |> 
      dplyr::filter(visit_code == "1500", .feature == "colonized_LBP_by_mg")
  ) |> 
  mutate(LBP_colonization_by_week5 = outcome |> replace_na(FALSE))
Code 
summary_table <- 
  col_by_week5 |> 
  group_by(site, arm) |> 
  summarize(
    n = n(), 
    n_success = sum(LBP_colonization_by_week5),
    .groups = "drop"
  ) |> 
  mutate(
    p = n_success / n,
    CI = binom::binom.confint(n_success, n, method = "exact")
  )
Code 
table_2 <- 
  summary_table |> 
  mutate(
    N_per_site_and_arm = str_c("N = ", n),
    LBP_strain_detected = str_c(n_success, " (", round(p * 100)," %)"),
    CI_text = str_c(round(CI$lower * 100), "% - ", round(CI$upper * 100), "%")
  ) |> 
  dplyr::select(
    site, arm, N_per_site_and_arm, LBP_strain_detected, CI_text
  ) |> 
  pivot_longer(-c(site, arm)) |>
  mutate(
    name = 
      case_when(
        name == "N_per_site_and_arm" ~ "N participants",
        name == "LBP_strain_detected" ~ "n (%) participants\nwith LBP strain detected",
        name == "CI_text" ~ "95% CI"
      )
  ) |> 
  pivot_wider(
    id_cols = c(site, name), 
    names_from = arm,
    values_from = value, values_fill = ""
    )
Code 
table_2 |> 
  group_by(site) |> 
  gt(caption = "Table 1: Colonization by week 5 by arm and site", row_group_as_column = TRUE) |> 
  cols_width(
    "name" ~ px(200),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    # Blinded = "All blinded arms",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )
Table 1: Colonization by week 5 by arm and site
Placebo LC-106
7 days		 LC-106
3 days		 LC-106
overlap		 LC-115
7 days
CAP N participants N = 14 N = 14 N = 14 N = 14 N = 14
n (%) participants with LBP strain detected 0 (0 %) 9 (64 %) 7 (50 %) 7 (50 %) 10 (71 %)
95% CI 0% - 23% 35% - 87% 23% - 77% 23% - 77% 42% - 92%
MGH N participants N = 5 N = 7 N = 1 N = 1 N = 6
n (%) participants with LBP strain detected 1 (20 %) 6 (86 %) 1 (100 %) 1 (100 %) 6 (100 %)
95% CI 1% - 72% 42% - 100% 3% - 100% 3% - 100% 54% - 100%

Visualization of the primary outcome

Code 
col_by_week5 |> 
  arrange(site, arm, LBP_colonization_by_week5) |>
  group_by(site, arm) |> 
  mutate(participant_number = row_number() |> factor()) |> 
  ungroup() |> 
  ggplot(aes(x = participant_number, y = site |> fct_rev(), col = site, fill = site, shape = LBP_colonization_by_week5)) +
  geom_point(size = 3) +
  facet_grid(. ~ arm, scales = "free_x", space = "free_x") +
  guides(color = "none", fill = "none") +
  scale_shape_manual("Colonized by LBP by week 5", values = c(1, 16)) +
  scale_color_manual(values = site_colors) + 
  xlab("Participant number") + ylab("") +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
  )

Tests

We test for differences between the placebo arm and each active arm for the primary outcome using Fisher’s exact test (exhaustive permutation test).

Data from both site are merged for each arm given the low numbers of participants at MGH.

The \(p\)-values are adjusted for multiple testing (multiple active arms against the Placebo) using the Benjamini-Hochberg method.

Code 
arms_to_test <- col_by_week5$arm |> unique() |> setdiff("Pl")

fisher_test_results <-
  map(
  arms_to_test,
  ~ {
    arm_to_test <- .x
    tmp <- col_by_week5 |> filter(arm %in% c("Pl", arm_to_test))
    table_for_test <- table(tmp$LBP_colonization_by_week5, tmp$arm |> fct_drop())
    test_res <- fisher.test(table_for_test, alternative = "greater")
    tibble(
      arm = arm_to_test,
      p_value = test_res$p.value,
      OR = test_res$estimate,
      lower_CI = test_res$conf.int[1],
      upper_CI = test_res$conf.int[2]
    )
  }
) |> 
  bind_rows() |> 
  mutate(
    adj_p_value = p_value |> p.adjust(method = "BH")
  )
Code 
test_results <- 
  fisher_test_results |> 
  mutate(
    adj_p_value = adj_p_value |> scales::pvalue(accuracy = 0.001),
    OR = OR |> round(2) |> as.character(),
    CI = str_c(lower_CI |> round(2), " - ", upper_CI |> round(2))
    ) |> 
  select(arm, adj_p_value, OR, CI) |>
  pivot_longer(cols = -arm, names_to = "name", values_to = "value") |>
  pivot_wider(names_from = arm, values_from = value) |> 
  mutate(
    name = 
      case_when(
        name == "adj_p_value" ~ "Adj. p-value",
        name == "OR" ~ "OR",
        name == "CI" ~ "95% CI",
        TRUE ~ name
      )
  )
Code 
table_2_with_model_results <- 
  bind_rows(
    table_2,
    test_results
  ) |> 
  mutate(
    site = site |> str_replace_na(""),
    Pl = 
      case_when(
        name == "Adj. p-value" ~ "Ref.",
        name == "OR" ~ "",
        str_detect(name, "CI") ~ "",
        TRUE ~ Pl
      )
  )
Code 
table_2_with_model_results |> 
  group_by(site) |>
  gt(caption = "Table 2: Colonization by week 5 by arm and site", row_group_as_column = TRUE) |> 
  cols_width(
    "name" ~ px(150),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )
Table 2: Colonization by week 5 by arm and site
Placebo LC-106
7 days		 LC-106
3 days		 LC-106
overlap		 LC-115
7 days
CAP N participants N = 14 N = 14 N = 14 N = 14 N = 14
n (%) participants with LBP strain detected 0 (0 %) 9 (64 %) 7 (50 %) 7 (50 %) 10 (71 %)
95% CI 35% - 87% 23% - 77% 23% - 77% 42% - 92%
MGH N participants N = 5 N = 7 N = 1 N = 1 N = 6
n (%) participants with LBP strain detected 1 (20 %) 6 (86 %) 1 (100 %) 1 (100 %) 6 (100 %)
95% CI 42% - 100% 3% - 100% 3% - 100% 54% - 100%
Adj. p-value Ref. <0.001 0.002 0.002 <0.001
OR 39.76 18.61 18.61 60.46
95% CI 5.69 - Inf 2.48 - Inf 2.48 - Inf 8.17 - Inf

Model

[This section still needs more work given the low sample size in some of the arms. Code is not executed].

Code 
col_by_week5_agg <- 
  col_by_week5 |>
  group_by(site, arm) |>
  summarise(success = LBP_colonization_by_week5 |> sum(), ntrials = n()) 

bfit <- 
  brm(
    success | trials(ntrials) ~ arm + site + site:arm, 
    data = col_by_week5_agg, 
    family = binomial("logit")
    )
Code 
summary(bfit)
plot(bfit, ask = FALSE)
bind_cols(col_by_week5_agg, predict(bfit))
# loo(bfit, moment_match = TRUE, reloo = TRUE)
# pp_check(bfit)
Code 
plot_res <- plot(conditional_effects(bfit), ask = FALSE)
Code 
plot_res <- lapply(plot_res, function(p) {
  p + scale_color_manual(values = site_colors)
})

Reduce(`+`, plot_res) + 
  plot_layout(widths = c(2.2, 1, 3))
Code 
model_results <- summary(bfit)$fixed
model_results <- 
  model_results |> 
  rownames_to_column("term") |> 
  as_tibble() |> 
  mutate(
    factor = 
      case_when(
        str_detect(term, ":site") ~ "Arm x Site",
        str_detect(term, "^site") ~ "Site",
        str_detect(term, "^arm") ~ "Arm",
        TRUE ~ "Ref.",
      ),
    factor_level = 
      term |> 
      str_remove("arm") |> str_remove("site") |> str_replace(":"," x ") |> 
      str_replace("6M", "6-") |> str_replace("CM", "C-")
  ) |> 
  mutate(
    OR = ifelse(factor == "Ref.", NA, exp(Estimate)),
    lower_CI = ifelse(factor == "Ref.", NA, exp(`l-95% CI`)),
    upper_CI = ifelse(factor == "Ref.", NA, exp(`u-95% CI`))
  )
Code 
model_results |> 
  select(factor, factor_level, OR, lower_CI, upper_CI) |>
  gt() |> 
  gt::fmt_number(
    decimals = 2, n_sigfig = 3
  )
Code 
model_results_arm <- 
  model_results |> 
  filter(factor == "Arm") |> 
  mutate(
    OR = OR |> round(2) |> as.character(),
    CI = str_c(lower_CI |> round(2), " - ", upper_CI |> round(2))
    ) |> 
  select(factor_level, OR, CI) |>
  pivot_longer(cols = -factor_level, names_to = "name", values_to = "value") |>
  pivot_wider(names_from = factor_level, values_from = value)
Code 
table_2_with_model_results <- 
  bind_rows(
    table_2,
    model_results_arm
  ) |> 
  mutate(
    site = site |> str_replace_na(""),
    Pl = 
      case_when(
        name == "OR" ~ "Ref.",
        name == "CI" ~ "",
        TRUE ~ Pl
      )
  )
Code 
table_2_with_model_results |> 
  group_by(site) |>
  gt(caption = "Table 2: Colonization by week 5 by arm and site", row_group_as_column = TRUE) |> 
  cols_width(
    "name" ~ px(200),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )

Sensitivity analyses (Supplementary tables and figures)

LBP colonization by week 5 excluding immediately post-product visits

Defining the alternative primary outcome

Code 
new_outcome_def <- 
  # we start from the "colonized at"
  mae[["col_LBP_mg"]] |> 
  as_tibble() |> 
  dplyr::filter(.feature == "colonized_LBP_at_mg") |> 
  dplyr::left_join(
    mae@colData |> as_tibble() |> select(uid, pid, visit_code, arm),
    by = join_by(.sample == uid)
  ) |> 
  filter(!is.na(arm))

# we fill the missing visits
new_outcome_def <- 
  new_outcome_def |> 
  select(-c(arm)) |> 
  dplyr::full_join(
    new_outcome_def |> 
      select(pid, arm) |> 
      distinct() |> 
      expand_grid(
        visit_code = 
          unique(new_outcome_def$visit_code) |> sort() |> setdiff(c("0001","0010"))
        ),
    by = join_by(pid, visit_code)
  ) |> 
  arrange(pid, visit_code) |> 
  mutate(outcome = outcome |> replace_na(FALSE)) |> 
  dplyr::filter(!is.na(arm))
  
# we compute the new outcome
new_outcome_def <- 
  new_outcome_def |>  
  arrange(pid, visit_code) |> 
  group_by(pid) |> 
  mutate(
    tmp = 
      case_when(
        (arm %in% c("Pl", "LC-106-7", "LC-115")) & (as.numeric(visit_code) <= 1200) ~ FALSE,
        (arm %in% c("LC-106-3", "LC-106-o")) & (as.numeric(visit_code) < 1200) ~ FALSE,
        TRUE ~ outcome
      ),
    colonized_LBP_by_mg = cummax(tmp) |> as.logical()
  ) |> 
  ungroup()
Code 
new_outcome_def |> 
  left_join(mae@colData |> as_tibble() |> select(pid, mITT) |> distinct()) |>
  ggplot() +
  aes(y = visit_code |> fct_rev(), x = pid, fill = colonized_LBP_by_mg) +
  facet_grid(. ~ arm + ifelse(mITT, "mITT", "ITT"), scales = "free", space = "free") +
  geom_tile() + 
  scale_fill_manual(
    "Colonized by LBP by MG by each visit",
    values = c("gray", "orangered"), labels = c("No", "Yes")
    ) +
  ylab("Visit code") + xlab("Participant IDs") +
  labs(
    caption = "Colonization at missed visits was considered negative."
  ) +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
    legend.position = "bottom"
  )

Code 
new_outcome_def <- 
  new_outcome_def |> 
  dplyr::rename(colonized_LBP_by_mg_alt = colonized_LBP_by_mg) |> 
  left_join(
    mae[["col_LBP_mg"]] |> 
      as_tibble() |> 
      dplyr::filter(.feature == "colonized_LBP_by_mg") |> 
      select(.sample, outcome) |> 
      dplyr::rename(colonized_LBP_by_mg = outcome)
  )
Code 
new_outcome_def |> 
  dplyr::filter(!is.na(colonized_LBP_by_mg)) |> 
  dplyr::count(colonized_LBP_by_mg_alt, colonized_LBP_by_mg)
new_outcome_def |> 
  dplyr::filter(visit_code == "1500", !is.na(colonized_LBP_by_mg)) |>
  dplyr::count(colonized_LBP_by_mg_alt, colonized_LBP_by_mg)
Code 
col_by_week5 <- 
  new_outcome_def |> 
  select(pid, visit_code, colonized_LBP_by_mg_alt) |> 
  left_join(
    mae@colData |> as_tibble() |> 
      select(uid, pid, visit_code, site, randomized, arm, ITT, mITT, PP)
    ) |> 
  dplyr::filter(randomized, mITT) |> 
  dplyr::filter(visit_code == "1500") |> 
  mutate(LBP_colonization_by_week5 = colonized_LBP_by_mg_alt |> replace_na(FALSE))

Visualization of the alternative primary outcome

Code 
col_by_week5 |> 
  arrange(site, arm, LBP_colonization_by_week5) |>
  group_by(site, arm) |> 
  mutate(participant_number = row_number() |> factor()) |> 
  ungroup() |> 
  ggplot(aes(x = participant_number, y = site |> fct_rev(), col = site, fill = site, shape = LBP_colonization_by_week5)) +
  geom_point(size = 3) +
  facet_grid(. ~ arm, scales = "free_x", space = "free_x") +
  guides(color = "none", fill = "none") +
  scale_shape_manual("Colonized by LBP by week 5 (excluding immediately post-product visit)", values = c(1, 16)) +
  scale_color_manual(values = site_colors) + 
  xlab("Participant number") + ylab("") +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
  )

Table

Code 
summary_table <- 
  col_by_week5 |> 
  group_by(site, arm) |> 
  summarize(
    n = n(), 
    n_success = sum(LBP_colonization_by_week5),
    .groups = "drop"
  ) |> 
  mutate(
    p = n_success / n,
    CI = binom::binom.confint(n_success, n, method = "exact")
  )
Code 
table_2 <- 
  summary_table |> 
  mutate(
    N_per_site_and_arm = str_c("N = ", n),
    LBP_strain_detected = str_c(n_success, " (", round(p * 100)," %)"),
    CI_text = str_c(round(CI$lower * 100), "% - ", round(CI$upper * 100), "%")
  ) |> 
  dplyr::select(
    site, arm, N_per_site_and_arm, LBP_strain_detected, CI_text
  ) |> 
  pivot_longer(-c(site, arm)) |>
  mutate(
    name = 
      case_when(
        name == "N_per_site_and_arm" ~ "N participants",
        name == "LBP_strain_detected" ~ "n (%) participants\nwith LBP strain detected",
        name == "CI_text" ~ "95% CI"
      )
  ) |> 
  pivot_wider(
    id_cols = c(site, name), 
    names_from = arm,
    values_from = value, values_fill = ""
    )
Code 
arms_to_test <- col_by_week5$arm |> unique() |> setdiff("Pl")

fisher_test_results <-
  map(
  arms_to_test,
  ~ {
    arm_to_test <- .x
    tmp <- col_by_week5 |> filter(arm %in% c("Pl", arm_to_test))
    table_for_test <- table(tmp$LBP_colonization_by_week5, tmp$arm |> fct_drop())
    test_res <- fisher.test(table_for_test, alternative = "greater")
    tibble(
      arm = arm_to_test,
      p_value = test_res$p.value,
      OR = test_res$estimate,
      lower_CI = test_res$conf.int[1],
      upper_CI = test_res$conf.int[2]
    )
  }
) |> 
  bind_rows() |> 
  mutate(
    adj_p_value = p_value |> p.adjust(method = "BH")
  )
Code 
test_results <- 
  fisher_test_results |> 
  mutate(
    adj_p_value = adj_p_value |> scales::pvalue(accuracy = 0.001),
    OR = OR |> round(2) |> as.character(),
    CI = str_c(lower_CI |> round(2), " - ", upper_CI |> round(2))
    ) |> 
  select(arm, adj_p_value, OR, CI) |>
  pivot_longer(cols = -arm, names_to = "name", values_to = "value") |>
  pivot_wider(names_from = arm, values_from = value) |> 
  mutate(
    name = 
      case_when(
        name == "adj_p_value" ~ "Adj. p-value",
        name == "OR" ~ "OR",
        name == "CI" ~ "95% CI",
        TRUE ~ name
      )
  )
Code 
table_2_with_model_results <- 
  bind_rows(
    table_2,
    test_results |> filter(name != "Adj. p-value")
  ) |> 
  mutate(
    site = site |> str_replace_na(""),
    Pl = 
      case_when(
        name == "Adj. p-value" ~ "Ref.",
        name == "OR" ~ "Ref.",
        str_detect(name, "CI") ~ "",
        TRUE ~ Pl
      )
  )
Code 
table_2_with_model_results |> 
  group_by(site) |>
  gt(
    caption = "LBP Colonization by week 5 by arm and site excluding immediately post-product visit.", 
    row_group_as_column = TRUE
    ) |> 
  cols_width(
    "name" ~ px(150),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )
LBP Colonization by week 5 by arm and site excluding immediately post-product visit.
Placebo LC-106
7 days		 LC-106
3 days		 LC-106
overlap		 LC-115
7 days
CAP N participants N = 14 N = 14 N = 14 N = 14 N = 14
n (%) participants with LBP strain detected 0 (0 %) 8 (57 %) 7 (50 %) 7 (50 %) 6 (43 %)
95% CI 29% - 82% 23% - 77% 23% - 77% 18% - 71%
MGH N participants N = 5 N = 7 N = 1 N = 1 N = 6
n (%) participants with LBP strain detected 1 (20 %) 4 (57 %) 1 (100 %) 1 (100 %) 4 (67 %)
95% CI 18% - 90% 3% - 100% 3% - 100% 22% - 96%
OR Ref. 22.09 18.61 18.61 16.73
95% CI 3.23 - Inf 2.48 - Inf 2.48 - Inf 2.41 - Inf

LBP colonization by week 5, qPCR-estimated relative abundances

These analyses include the immediate post-product visits.

Code 
# by qPCR 

col_by_week5_qPCR <-
  mae@colData |> as_tibble() |> 
  select(pid, site, randomized, arm, ITT, mITT, PP) |> 
  distinct() |> 
  filter(randomized, mITT) |> 
  left_join(
    mae[["col_LBP_qPCR"]] |> 
      as_tibble() |> 
      dplyr::left_join(
        mae@colData |> as_tibble() |> select(uid, pid, visit_code),
        by = join_by(.sample == uid)
      ) |> 
      dplyr::filter(visit_code == "1500", .feature == "colonized_LBP_by_qpcr")
  ) |> 
  mutate(LBP_colonization_by_week5 = outcome |> replace_na(FALSE))
Code 
col_by_week5_qPCR |> 
  arrange(site, arm, LBP_colonization_by_week5) |>
  group_by(site, arm) |> 
  mutate(participant_number = row_number() |> factor()) |> 
  ungroup() |> 
  ggplot(aes(x = participant_number, y = site |> fct_rev(), col = site, fill = site, shape = LBP_colonization_by_week5)) +
  geom_point(size = 3) +
  facet_grid(. ~ arm, scales = "free_x", space = "free_x") +
  guides(color = "none", fill = "none") +
  scale_shape_manual("Colonized by LBP by week 5", values = c(1, 16)) +
  scale_color_manual(values = site_colors) + 
  xlab("Participant number") + ylab("") +
  ggtitle("Colonization based on the relative abundances estimated by qPCR") +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), 
    plot.title = element_text(hjust = 0.5)
  )

Table

Code 
summary_table <- 
  col_by_week5_qPCR |> 
  group_by(site, arm) |> 
  summarize(
    n = n(), 
    n_success = sum(LBP_colonization_by_week5),
    .groups = "drop"
  ) |> 
  mutate(
    p = n_success / n,
    CI = binom::binom.confint(n_success, n, method = "exact")
  )
Code 
table_2 <- 
  summary_table |> 
  mutate(
    N_per_site_and_arm = str_c("N = ", n),
    LBP_strain_detected = str_c(n_success, " (", round(p * 100)," %)"),
    CI_text = str_c(round(CI$lower * 100), "% - ", round(CI$upper * 100), "%")
  ) |> 
  dplyr::select(
    site, arm, N_per_site_and_arm, LBP_strain_detected, CI_text
  ) |> 
  pivot_longer(-c(site, arm)) |>
  mutate(
    name = 
      case_when(
        name == "N_per_site_and_arm" ~ "N participants",
        name == "LBP_strain_detected" ~ "n (%) participants\nwith LBP strain detected",
        name == "CI_text" ~ "95% CI"
      )
  ) |> 
  pivot_wider(
    id_cols = c(site, name), 
    names_from = arm,
    values_from = value, values_fill = ""
    )
Code 
arms_to_test <- col_by_week5$arm |> unique() |> setdiff("Pl")

fisher_test_results <-
  map(
  arms_to_test,
  ~ {
    arm_to_test <- .x
    tmp <- col_by_week5_qPCR |> filter(arm %in% c("Pl", arm_to_test))
    table_for_test <- table(tmp$LBP_colonization_by_week5, tmp$arm |> fct_drop())
    test_res <- fisher.test(table_for_test, alternative = "greater")
    tibble(
      arm = arm_to_test,
      p_value = test_res$p.value,
      OR = test_res$estimate,
      lower_CI = test_res$conf.int[1],
      upper_CI = test_res$conf.int[2]
    )
  }
) |> 
  bind_rows() |> 
  mutate(
    adj_p_value = p_value |> p.adjust(method = "BH")
  )
Code 
test_results <- 
  fisher_test_results |> 
  mutate(
    adj_p_value = adj_p_value |> scales::pvalue(accuracy = 0.001),
    OR = OR |> round(2) |> as.character(),
    CI = str_c(lower_CI |> round(2), " - ", upper_CI |> round(2))
    ) |> 
  select(arm, adj_p_value, OR, CI) |>
  pivot_longer(cols = -arm, names_to = "name", values_to = "value") |>
  pivot_wider(names_from = arm, values_from = value) |> 
  mutate(
    name = 
      case_when(
        name == "adj_p_value" ~ "Adj. p-value",
        name == "OR" ~ "OR",
        name == "CI" ~ "95% CI",
        TRUE ~ name
      )
  )
Code 
table_2_with_model_results <- 
  bind_rows(
    table_2,
    test_results |> filter(name != "Adj. p-value")
  ) |> 
  mutate(
    site = site |> str_replace_na(""),
    Pl = 
      case_when(
        name == "Adj. p-value" ~ "Ref.",
        name == "OR" ~ "Ref.",
        str_detect(name, "CI") ~ "",
        TRUE ~ Pl
      )
  )
Code 
table_2_with_model_results |> 
  group_by(site) |>
  gt(
    caption = "LBP Colonization by week 5 by arm, with colonization definition based on qPCR-estimated relative abundance.", 
    row_group_as_column = TRUE
    ) |> 
  cols_width(
    "name" ~ px(150),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )
LBP Colonization by week 5 by arm, with colonization definition based on qPCR-estimated relative abundance.
Placebo LC-106
7 days		 LC-106
3 days		 LC-106
overlap		 LC-115
7 days
CAP N participants N = 14 N = 14 N = 14 N = 14 N = 14
n (%) participants with LBP strain detected 2 (14 %) 11 (79 %) 8 (57 %) 8 (57 %) 10 (71 %)
95% CI 49% - 95% 29% - 82% 29% - 82% 42% - 92%
MGH N participants N = 5 N = 7 N = 1 N = 1 N = 6
n (%) participants with LBP strain detected 1 (20 %) 6 (86 %) 1 (100 %) 1 (100 %) 6 (100 %)
95% CI 42% - 100% 3% - 100% 3% - 100% 54% - 100%
OR Ref. 20.25 7.44 7.44 19.07
95% CI 4.44 - Inf 1.62 - Inf 1.62 - Inf 4.15 - Inf

LBP detection by week 5, qPCR-based detection

In this analysis, LBP is considered to be detected if at least 2 LBP strains had observed Cq values at the same visit by week 5. “By week 5” includes all post-product visits from visit_code = 1200 (no excluding the immediate post-product visits.

Code 
detected_by_week5_qPCR <-
  mae@colData |> as_tibble() |> 
  select(pid, site, randomized, arm, ITT, mITT, PP) |> 
  distinct() |> 
  filter(randomized, mITT) |> 
  left_join(
    mae[["col_LBP_qPCR"]] |> 
      as_tibble() |> 
      dplyr::left_join(
        mae@colData |> as_tibble() |> select(uid, pid, visit_code),
        by = join_by(.sample == uid)
      ) |> 
      dplyr::filter(visit_code == "1500", .feature == "LBP_detected_by_qpcr")
  ) |> 
  mutate(LBP_detected_by_week5 = outcome |> replace_na(FALSE))
Code 
detected_by_week5_qPCR |> 
  arrange(site, arm, LBP_detected_by_week5) |>
  group_by(site, arm) |> 
  mutate(participant_number = row_number() |> factor()) |> 
  ungroup() |> 
  ggplot(aes(x = participant_number, y = site |> fct_rev(), col = site, fill = site, shape = LBP_detected_by_week5)) +
  geom_point(size = 3) +
  facet_grid(. ~ arm, scales = "free_x", space = "free_x") +
  guides(color = "none", fill = "none") +
  scale_shape_manual("Colonized by LBP by week 5", values = c(1, 16)) +
  scale_color_manual(values = site_colors) + 
  xlab("Participant number") + ylab("") +
  ggtitle("Colonization based on qPCR-based detection of at least 2 LBP strains detected at the same visit by week 5") +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), 
    plot.title = element_text(hjust = 0.5)
  )

Table

Code 
summary_table <- 
  detected_by_week5_qPCR |> 
  group_by(site, arm) |> 
  summarize(
    n = n(), 
    n_success = sum(LBP_detected_by_week5),
    .groups = "drop"
  ) |> 
  mutate(
    p = n_success / n,
    CI = binom::binom.confint(n_success, n, method = "exact")
  )
Code 
table_2 <- 
  summary_table |> 
  mutate(
    N_per_site_and_arm = str_c("N = ", n),
    LBP_strain_detected = str_c(n_success, " (", round(p * 100)," %)"),
    CI_text = str_c(round(CI$lower * 100), "% - ", round(CI$upper * 100), "%")
  ) |> 
  dplyr::select(
    site, arm, N_per_site_and_arm, LBP_strain_detected, CI_text
  ) |> 
  pivot_longer(-c(site, arm)) |>
  mutate(
    name = 
      case_when(
        name == "N_per_site_and_arm" ~ "N participants",
        name == "LBP_strain_detected" ~ "n (%) participants\nwith LBP strain detected",
        name == "CI_text" ~ "95% CI"
      )
  ) |> 
  pivot_wider(
    id_cols = c(site, name), 
    names_from = arm,
    values_from = value, values_fill = ""
    )
Code 
arms_to_test <- col_by_week5$arm |> unique() |> setdiff("Pl")

fisher_test_results <-
  map(
  arms_to_test,
  ~ {
    arm_to_test <- .x
    tmp <- detected_by_week5_qPCR |> filter(arm %in% c("Pl", arm_to_test))
    table_for_test <- table(tmp$LBP_detected_by_week5, tmp$arm |> fct_drop())
    test_res <- fisher.test(table_for_test, alternative = "greater")
    tibble(
      arm = arm_to_test,
      p_value = test_res$p.value,
      OR = test_res$estimate,
      lower_CI = test_res$conf.int[1],
      upper_CI = test_res$conf.int[2]
    )
  }
) |> 
  bind_rows() |> 
  mutate(
    adj_p_value = p_value |> p.adjust(method = "BH")
  )
Code 
test_results <- 
  fisher_test_results |> 
  mutate(
    adj_p_value = adj_p_value |> scales::pvalue(accuracy = 0.001),
    OR = OR |> round(2) |> as.character(),
    CI = str_c(lower_CI |> round(2), " - ", upper_CI |> round(2))
    ) |> 
  select(arm, adj_p_value, OR, CI) |>
  pivot_longer(cols = -arm, names_to = "name", values_to = "value") |>
  pivot_wider(names_from = arm, values_from = value) |> 
  mutate(
    name = 
      case_when(
        name == "adj_p_value" ~ "Adj. p-value",
        name == "OR" ~ "OR",
        name == "CI" ~ "95% CI",
        TRUE ~ name
      )
  )
Code 
table_2_with_model_results <- 
  bind_rows(
    table_2,
    test_results |> filter(name != "Adj. p-value")
  ) |> 
  mutate(
    site = site |> str_replace_na(""),
    Pl = 
      case_when(
        name == "Adj. p-value" ~ "Ref.",
        name == "OR" ~ "Ref.",
        str_detect(name, "CI") ~ "",
        TRUE ~ Pl
      )
  )
Code 
table_2_with_model_results |> 
  group_by(site) |>
  gt(
    caption = "LBP detection by week 5 by arm, with LBP detection defined as at least two LBP strains simultaneously detected by qPCR.", 
    row_group_as_column = TRUE
    ) |> 
  cols_width(
    "name" ~ px(150),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )
LBP detection by week 5 by arm, with LBP detection defined as at least two LBP strains simultaneously detected by qPCR.
Placebo LC-106
7 days		 LC-106
3 days		 LC-106
overlap		 LC-115
7 days
CAP N participants N = 14 N = 14 N = 14 N = 14 N = 14
n (%) participants with LBP strain detected 2 (14 %) 12 (86 %) 8 (57 %) 9 (64 %) 11 (79 %)
95% CI 57% - 98% 29% - 82% 35% - 87% 49% - 95%
MGH N participants N = 5 N = 7 N = 1 N = 1 N = 6
n (%) participants with LBP strain detected 3 (60 %) 6 (86 %) 1 (100 %) 1 (100 %) 6 (100 %)
95% CI 42% - 100% 3% - 100% 3% - 100% 54% - 100%
OR Ref. 15.28 4.01 5.29 14.44
95% CI 3.49 - Inf 0.99 - Inf 1.29 - Inf 3.28 - Inf

L. crispatus dominance by week 5

In this analysis, L. crispatus dominance is defined as a metagenomic-estimated relative abundance ≥ 50%. “By week 5” includes all post-product visits from visit_code = 1200 (no excluding the immediate post-product visits.

Code 
# crispatus dominance 

crisp_dom_by_5week <- 
  mae@colData |> as_tibble() |> 
  select(pid, site, randomized, arm, ITT, mITT, PP) |> 
  distinct() |> 
  filter(randomized, mITT) |> 
  left_join(
    mae[["col_crispatus_mg"]] |> 
      as_tibble() |> 
      dplyr::left_join(
        mae@colData |> as_tibble() |> select(uid, pid, visit_code),
        by = join_by(.sample == uid)
      ) |> 
      dplyr::filter(visit_code == "1500", .feature == "crispatus_dominance_by_mg")
  ) |> 
  mutate(crisp_dom_by_5week = outcome |> replace_na(FALSE))
Code 
crisp_dom_by_5week |> 
  arrange(site, arm, crisp_dom_by_5week) |>
  group_by(site, arm) |> 
  mutate(participant_number = row_number() |> factor()) |> 
  ungroup() |> 
  ggplot(aes(x = participant_number, y = site |> fct_rev(), col = site, fill = site, shape = crisp_dom_by_5week)) +
  geom_point(size = 3) +
  facet_grid(. ~ arm, scales = "free_x", space = "free_x") +
  guides(color = "none", fill = "none") +
  scale_shape_manual("Colonized by LBP by week 5", values = c(1, 16)) +
  scale_color_manual(values = site_colors) + 
  xlab("Participant number") + ylab("") +
  ggtitle("L. crispatus dominance (≥ 50% by metagenomics)") +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), 
    plot.title = element_text(hjust = 0.5)
  )

Table

Code 
summary_table <- 
  crisp_dom_by_5week |> 
  group_by(site, arm) |> 
  summarize(
    n = n(), 
    n_success = sum(crisp_dom_by_5week),
    .groups = "drop"
  ) |> 
  mutate(
    p = n_success / n,
    CI = binom::binom.confint(n_success, n, method = "exact")
  )
Code 
table_2 <- 
  summary_table |> 
  mutate(
    N_per_site_and_arm = str_c("N = ", n),
    Lc_dom = str_c(n_success, " (", round(p * 100)," %)"),
    CI_text = str_c(round(CI$lower * 100), "% - ", round(CI$upper * 100), "%")
  ) |> 
  dplyr::select(
    site, arm, N_per_site_and_arm, Lc_dom, CI_text
  ) |> 
  pivot_longer(-c(site, arm)) |>
  mutate(
    name = 
      case_when(
        name == "N_per_site_and_arm" ~ "N participants",
        name == "Lc_dom" ~ "n (%) participants\n≥ 50% L. crispatus",
        name == "CI_text" ~ "95% CI"
      )
  ) |> 
  pivot_wider(
    id_cols = c(site, name), 
    names_from = arm,
    values_from = value, values_fill = ""
    )
Code 
arms_to_test <- col_by_week5$arm |> unique() |> setdiff("Pl")

fisher_test_results <-
  map(
  arms_to_test,
  ~ {
    arm_to_test <- .x
    tmp <- crisp_dom_by_5week |> filter(arm %in% c("Pl", arm_to_test))
    table_for_test <- table(tmp$crisp_dom_by_5week, tmp$arm |> fct_drop())
    test_res <- fisher.test(table_for_test, alternative = "greater")
    tibble(
      arm = arm_to_test,
      p_value = test_res$p.value,
      OR = test_res$estimate,
      lower_CI = test_res$conf.int[1],
      upper_CI = test_res$conf.int[2]
    )
  }
) |> 
  bind_rows() |> 
  mutate(
    adj_p_value = p_value |> p.adjust(method = "BH")
  )
Code 
test_results <- 
  fisher_test_results |> 
  mutate(
    adj_p_value = adj_p_value |> scales::pvalue(accuracy = 0.001),
    OR = OR |> round(2) |> as.character(),
    CI = str_c(lower_CI |> round(2), " - ", upper_CI |> round(2))
    ) |> 
  select(arm, adj_p_value, OR, CI) |>
  pivot_longer(cols = -arm, names_to = "name", values_to = "value") |>
  pivot_wider(names_from = arm, values_from = value) |> 
  mutate(
    name = 
      case_when(
        name == "adj_p_value" ~ "Adj. p-value",
        name == "OR" ~ "OR",
        name == "CI" ~ "95% CI",
        TRUE ~ name
      )
  )
Code 
table_2_with_model_results <- 
  bind_rows(
    table_2,
    test_results |> filter(name != "Adj. p-value")
  ) |> 
  mutate(
    site = site |> str_replace_na(""),
    Pl = 
      case_when(
        name == "Adj. p-value" ~ "Ref.",
        name == "OR" ~ "Ref.",
        str_detect(name, "CI") ~ "",
        TRUE ~ Pl
      )
  )
Code 
table_2_with_model_results |> 
  group_by(site) |>
  gt(
    caption = "Ever ≥50% L. crispatus by week 5 by arm.", 
    row_group_as_column = TRUE
    ) |> 
  cols_width(
    "name" ~ px(150),
    everything() ~ px(120)
  ) |> 
  cols_label(
    name = "",
    Pl = "Placebo",
    `LC-106-7` = "LC-106<br>7 days",
    `LC-106-3` = "LC-106<br>3 days",
    `LC-106-o` = "LC-106<br>overlap",
    `LC-115` = "LC-115<br>7 days",
    .fn = md
  )
Ever ≥50% L. crispatus by week 5 by arm.
Placebo LC-106
7 days		 LC-106
3 days		 LC-106
overlap		 LC-115
7 days
CAP N participants N = 14 N = 14 N = 14 N = 14 N = 14
n (%) participants ≥ 50% L. crispatus 0 (0 %) 8 (57 %) 5 (36 %) 6 (43 %) 10 (71 %)
95% CI 29% - 82% 13% - 65% 18% - 71% 42% - 92%
MGH N participants N = 5 N = 7 N = 1 N = 1 N = 6
n (%) participants ≥ 50% L. crispatus 1 (20 %) 6 (86 %) 1 (100 %) 1 (100 %) 5 (83 %)
95% CI 42% - 100% 3% - 100% 3% - 100% 36% - 100%
OR Ref. 32.33 11.13 14.44 46.74
95% CI 4.69 - Inf 1.43 - Inf 1.9 - Inf 6.51 - Inf

Secondary outcomes (Figures 2-4)

Code 
fig_title <- mae_title(mae)

Kinetics of LBP colonization

Total relative abundances of LBP strains

Code 
total_rel_ab_of_LBP <- 
  mae[["mg"]] |> 
  as_tibble() |> 
  filter(!is.na(LBP), !poor_quality_mg_data) |> 
  left_join(
    mae |> colData() |> as_tibble(), 
    by = join_by(uid)
  ) |>
  filter(randomized, mITT) |> 
  group_by(.sample, uid) |>
  summarize(
    total_LBP_rel_ab = sum(rel_ab, na.rm = TRUE),
    .groups = "drop"
  )

total_rel_ab_of_LBP <- 
  total_rel_ab_of_LBP |> 
  dplyr::left_join(mae |> colData() |> as_tibble(), by = join_by(uid))


total_rel_ab_of_LBP <- 
  total_rel_ab_of_LBP |> 
  left_join(
    total_rel_ab_of_LBP |> 
      select(pid, arm, site) |> 
      distinct() |> 
      group_by(arm, site) |> 
      arrange(pid) |> 
      mutate(pid_nb = row_number()) |> 
      ungroup()
  )
Code 
g_tot_prop_LBP_strains <- 
  total_rel_ab_of_LBP |>
  filter(visit_type == "Clinic", !is.na(arm), PIPV) |> 
  ggplot(aes(x = study_week, y = total_LBP_rel_ab, col = arm)) +
  geom_line(aes(group = pid), alpha = 0.5) +
  geom_point(size = 0.5, alpha = 0.5) +
  facet_grid(site ~ arm) + 
  scale_x_continuous("Study weeks", breaks = c(-1:5,7,9,12), minor_breaks = 0:12) +
  scale_y_continuous(
    "Total relative abundances\nof LBP strains", labels = scales::percent,
    breaks = seq(0, 1, by = 0.2)
  ) +
  expand_limits(y = 0) +
  scale_color_manual(values = arm_colors) +
  guides(col = "none") +
  ggtitle(fig_title)
Code 
g_tot_prop_LBP_strains

Code 
g_tot_prop_LBP_strains +
  geom_label(aes(label = pid_nb), size = 2) +
  labs(
    caption = "Participants are labeled by their enrollment order within each arm and site."
  )

Proportions of participants who colonized in each arm and each visit

Code 
get_longitudinal_proportions_of_participants_who_colonized <- 
  function(total_rel_ab_of_LBP) {
    total_rel_ab_of_LBP |> 
      filter(!is.na(arm), PIPV) |> 
      group_by(site, arm, study_week) |>
      summarize(
        prop_who_colonized_at = mean(total_LBP_rel_ab > 0.05), 
        CI_low = 
          prop.test(
            x = sum(total_LBP_rel_ab > 0.05), n = n(), 
            conf.level = 0.95)$conf.int[1],
        CI_high = 
          prop.test(
            x = sum(total_LBP_rel_ab > 0.05), n = n(), 
            conf.level = 0.95)$conf.int[2],
        .groups = "drop"
      )
  }
Code 
plot_longitudinal_proportions_of_participants_who_colonized <- function(total_rel_ab_of_LBP){
    
    get_longitudinal_proportions_of_participants_who_colonized(total_rel_ab_of_LBP) |>
      ggplot() +
      aes(x = study_week, y = prop_who_colonized_at, col = arm) +
      facet_grid(site ~ .) +
      geom_ribbon(
        aes(ymin = CI_low, ymax = CI_high, fill = arm), 
        alpha = 0.1, color = NA
      ) +
      geom_line() +
      geom_point() + 
      scale_x_continuous("Study weeks", breaks = c(-1:5,7,9,12), minor_breaks = 0:12) +
      scale_y_continuous(
        "Proportion of participants\nwith detected LBP strains", labels = scales::percent,
        breaks = seq(0, 1, by = 0.2)
      )  +
      expand_limits(y = 0) +
      scale_color_manual("Arm", values = arm_colors) +
      scale_fill_manual("Arm", values = arm_colors) 
  }
Code 
g_prop_colonized_by <- plot_longitudinal_proportions_of_participants_who_colonized(total_rel_ab_of_LBP)
g_prop_colonized_by + ggtitle(fig_title)

Code 
g_prop_colonized_by <- 
  plot_longitudinal_proportions_of_participants_who_colonized(
    total_rel_ab_of_LBP |> filter(!((arm %in% c("LC-106-3", "LC-106-o")) & (site == "MGH")))
    )
g_prop_colonized_by + ggtitle(fig_title)

Code 
plot_longitudinal_proportions_of_participants_who_colonized_v2 <- 
  function(total_rel_ab_of_LBP){
    
    get_longitudinal_proportions_of_participants_who_colonized(total_rel_ab_of_LBP) |>
      ggplot(aes(x = study_week, y = prop_who_colonized_at, col = arm)) +
      geom_linerange(
        aes(ymin = CI_low, ymax = CI_high, col = arm), 
        alpha = 0.4, lineend = "round", linewidth = 1,
        position = position_dodge(width = 0.5)
      ) +
      geom_line(position = position_dodge(width = 0.5), linewidth = 0.8) +
      geom_point(aes(shape = arm), position = position_dodge(width = 0.5), size = 2) +
      facet_grid(site ~ .) + 
      scale_shape_manual("Arm", values = c(1, 19, 15, 17, 8)) +
      scale_x_continuous("Study weeks", breaks = c(-1:5,7,9,12), minor_breaks = 0:12) +
      scale_y_continuous(
        "Proportion of participants\nwith detected LBP strains", labels = scales::percent,
        breaks = seq(0, 1, by = 0.2)
      ) +
      expand_limits(y = 0) +
      scale_color_manual("Arm", values = arm_colors) +
      scale_fill_manual("Arm", values = arm_colors)
    
  }

Alternative visualization:

Code 
g_prop_colonized_by <- plot_longitudinal_proportions_of_participants_who_colonized_v2(total_rel_ab_of_LBP)
g_prop_colonized_by +  ggtitle(fig_title)

Code 
g_prop_colonized_by <- 
  plot_longitudinal_proportions_of_participants_who_colonized_v2(
    total_rel_ab_of_LBP |> filter(!((arm %in% c("LC-106-3", "LC-106-o")) & (site == "MGH")))
    )

g_prop_colonized_by +  ggtitle(fig_title)

Kinetics of L. crispatus colonization

Total relative abundances of L. crispatus

Code 
total_rel_ab_of_Lc <- 
  mae[["mg"]] |> 
  as_tibble() |> 
  filter(str_detect(species, "crispatus"), !poor_quality_mg_data) |> 
  left_join(
    mae |> colData() |> as_tibble(), 
    by = join_by(uid)
  ) |>
  filter(randomized, mITT) |> 
  group_by(.sample, uid) |>
  summarize(
    total_Lc_rel_ab = sum(rel_ab, na.rm = TRUE),
    .groups = "drop"
  )

total_rel_ab_of_Lc <- 
  total_rel_ab_of_Lc |> 
  dplyr::left_join(mae |> colData() |> as_tibble(), by = join_by(uid))


# we add a pid_nb 
total_rel_ab_of_Lc <- 
  total_rel_ab_of_Lc |> 
  left_join(
    total_rel_ab_of_Lc |> 
      select(pid, arm, site) |> 
      distinct() |> 
      group_by(arm, site) |> 
      arrange(pid) |> 
      mutate(pid_nb = row_number()) |> 
      ungroup()
  )
Code 
g_tot_prop_Lc <- 
  total_rel_ab_of_Lc |>
  filter(visit_type == "Clinic", !is.na(arm), PIPV) |> 
  ggplot(aes(x = study_week, y = total_Lc_rel_ab, col = arm)) +
  geom_line(aes(group = pid), alpha = 0.5) +
  geom_point(size = 0.5, alpha = 0.5) +
  facet_grid(site ~ arm) + 
  scale_x_continuous("Study weeks", breaks = c(-1:5,7,9,12), minor_breaks = 0:12) +
  scale_y_continuous(
    "Total relative abundances\nof L. crispatus", labels = scales::percent,
    breaks = seq(0, 1, by = 0.2)
  ) +
  expand_limits(y = 0) +
  scale_color_manual(values = arm_colors) +
  guides(col = "none") +
  ggtitle(mae_title(mae))
Code 
g_tot_prop_Lc

Code 
g_tot_prop_Lc +
  geom_label(aes(label = pid_nb), size = 2) +
  labs(
    caption = "Participants are labeled by their enrollment order within each arm and site."
  )

Proportions of participants who colonized with 50% L. crispatus in each arm and each visit

Code 
get_longitudinal_proportions_of_participants_who_colonized <- 
  function(total_rel_ab_of_Lc) {
    total_rel_ab_of_Lc |> 
      filter(!is.na(arm), PIPV) |> 
      group_by(site, arm, study_week) |>
      summarize(
        prop_who_colonized_at = mean(total_Lc_rel_ab > 0.5), 
        CI_low = 
          prop.test(
            x = sum(total_Lc_rel_ab > 0.5), n = n(), 
            conf.level = 0.95)$conf.int[1],
        CI_high = 
          prop.test(
            x = sum(total_Lc_rel_ab > 0.5), n = n(), 
            conf.level = 0.95)$conf.int[2],
        .groups = "drop"
      )
  }
Code 
plot_longitudinal_proportions_of_participants_who_colonized <- function(total_rel_ab_of_Lc){
    
    get_longitudinal_proportions_of_participants_who_colonized(total_rel_ab_of_Lc) |>
      ggplot() +
      aes(x = study_week, y = prop_who_colonized_at, col = arm) +
      facet_grid(site ~ .) +
      geom_ribbon(
        aes(ymin = CI_low, ymax = CI_high, fill = arm), 
        alpha = 0.1, color = NA
      ) +
      geom_line() +
      geom_point() + 
      scale_x_continuous("Study weeks", breaks = c(-1:5,7,9,12), minor_breaks = 0:12) +
      scale_y_continuous(
        "Proportion of participants\n≥ 50% L. crispatus", labels = scales::percent,
        breaks = seq(0, 1, by = 0.2)
      )  +
      expand_limits(y = 0) +
      scale_color_manual("Arm", values = arm_colors) +
      scale_fill_manual("Arm", values = arm_colors) 
  }
Code 
g_prop_colonized_by <- plot_longitudinal_proportions_of_participants_who_colonized(total_rel_ab_of_Lc)
g_prop_colonized_by + ggtitle(fig_title)

Code 
g_prop_colonized_by <- 
  plot_longitudinal_proportions_of_participants_who_colonized(
    total_rel_ab_of_Lc |> filter(!((arm %in% c("LC-106-3", "LC-106-o")) & (site == "MGH")))
    )

g_prop_colonized_by + ggtitle(fig_title)

Alternative visualization:

Code 
plot_longitudinal_proportions_of_participants_who_colonized_v2 <- 
  function(total_rel_ab_of_Lc){
    
    get_longitudinal_proportions_of_participants_who_colonized(total_rel_ab_of_Lc) |>
      ggplot(aes(x = study_week, y = prop_who_colonized_at, col = arm)) +
      geom_linerange(
        aes(ymin = CI_low, ymax = CI_high, col = arm), 
        alpha = 0.4, lineend = "round", linewidth = 1,
        position = position_dodge(width = 0.5)
      ) +
      geom_line(position = position_dodge(width = 0.5), linewidth = 0.8) +
      geom_point(aes(shape = arm), position = position_dodge(width = 0.5), size = 2) +
      facet_grid(site ~ .) + 
      scale_shape_manual("Arm", values = c(1, 19, 15, 17, 8)) +
      scale_x_continuous("Study weeks", breaks = c(-1:5,7,9,12), minor_breaks = 0:12) +
      scale_y_continuous(
        "Proportion of participants\n≥ 50% L. crispatus", labels = scales::percent,
        breaks = seq(0, 1, by = 0.2)
      ) +
      expand_limits(y = 0) +
      scale_color_manual("Arm", values = arm_colors) +
      scale_fill_manual("Arm", values = arm_colors)
    
  }
Code 
g_prop_colonized_by <- plot_longitudinal_proportions_of_participants_who_colonized_v2(total_rel_ab_of_Lc)
g_prop_colonized_by + ggtitle(fig_title)

Code 
g_prop_colonized_by <- 
  plot_longitudinal_proportions_of_participants_who_colonized_v2(
    total_rel_ab_of_Lc |> filter(!((arm %in% c("LC-106-3", "LC-106-o")) & (site == "MGH")))
    )

g_prop_colonized_by + ggtitle(fig_title)

Durability of colonization

TODO: add/do figure 3D (include CIs)

= proportion of participants who colonized by week 5 that still have colonization by week 12.

Microbiota trajectories

Longitudinal stack relative abundances plot:

Code 
rel_abs <- 
  mae[["mg"]] |> 
  as_tibble() |> 
  select(
    .feature, .sample, uid, rel_ab, poor_quality_mg_data,
    taxon_label, genus, LBP, strain_origin
    )
Code 
rel_abs <- 
  rel_abs |> 
  group_by(.feature) |> 
  mutate(max_rel_ab = max(rel_ab, na.rm = TRUE), mean_rel_ab = mean(rel_ab, na.rm = TRUE)) |> 
  ungroup() |> 
  arrange(is.na(LBP), -mean_rel_ab) |> 
  mutate(.feature = .feature |> fct_inorder()) |> 
  mutate(
    taxon = 
      case_when(
        as.numeric(.feature) <= 29 ~ taxon_label,
        (genus == "Lactobacillus") ~ "Other Lactobacillus",
        TRUE ~ "Other non-Lacto."
      )
  ) |> 
  arrange(is.na(LBP), LBP, genus != "Lactobacillus", -mean_rel_ab) |> 
  mutate(taxon = taxon |> fct_inorder() |> fct_relevel("Other Lactobacillus", "Other non-Lacto.", "Other", after = Inf))
Code 
rel_abs_agg <- 
  rel_abs |> 
  group_by(uid, taxon, LBP, strain_origin, poor_quality_mg_data) |> 
  summarize(rel_ab = sum(rel_ab), .groups = "drop") |> 
  dplyr::left_join(mae |> colData() |> as_tibble(), by = join_by(uid))
Code 
g_trajectories <-
  rel_abs_agg |>
  filter(mITT, randomized != 0, PIPV, !poor_quality_mg_data) |>
  group_by(pid) |> 
  mutate(
    tot_LC115 = sum(rel_ab[LBP == "LC-115"]),
    tot_LBP = sum(rel_ab[!is.na(LBP)])
    ) |> 
  ungroup() |> 
  arrange(desc(PP), -tot_LC115, -tot_LBP) |> 
  mutate(
    pid = pid |> fct_inorder(),
    visit_label = ifelse(visit != "screening", str_c(visit, " (W", study_week, ")"), visit |> as.character()) |> fct_reorder(visit |> as.numeric()),
    ) |> 
  ggplot(aes(y = rel_ab, x = pid, fill = taxon, alpha = ifelse(PP, "PP", "mITT"))) +
  geom_col() +
  facet_grid(visit_label ~ arm + site, scales = "free", space = "free") +
  scale_fill_manual(
    values = get_taxa_colors(rel_abs_agg$taxon |> levels()),
    labels = get_taxa_labels(rel_abs_agg$taxon |> levels()),
    guide = guide_legend(ncol = 1)
    ) +
  scale_alpha_discrete("Population", range = c(0.5, 1)) +
  scale_y_continuous(
    "relative abundance", labels = scales::percent,
    breaks = seq(0, 0.5, by = 0.5), minor_breaks = seq(0, 1, by = 0.25)
  ) +
  scale_x_discrete("Participants") +
  theme(
    strip.text.y = element_text(angle = 0, hjust = 0),
    strip.text.x = element_text(angle = 90, hjust = 0),
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
  )
Code 
g_trajectories

Microbiota trajectories of mITT participants in the VIBRANT study. The relative abundances of the LBP strains and top taxa are shown. Participants are ordered by their total relative abundance of LBP strains, and the study weeks are shown in the rows. The alpha level (transparency) indicates whether the participant is part of the per-protocol (PP) or modified intention-to-treat (mITT) population.
Code 
# checking the data for the placebo participant that has the LBP strains at MGH

mae[["mg"]] |> 
  as_tibble() |> 
  dplyr::left_join(mae@colData |> as_tibble()) |> 
  filter(pid == "068100062", study_week == 5) 


mae[["qPCR"]] |> 
  as_tibble() |> 
  dplyr::left_join(mae@colData |> as_tibble()) |> 
  filter(pid == "068100062", study_week == 5) 

mae[["amplicon"]] |> 
  as_tibble() |> 
  dplyr::left_join(mae@colData |> as_tibble()) |> 
  filter(pid == "068100062", study_week == 5) |> 
  select(.feature, .sample, rel_ab, exclude_sample)


mae[["amplicon"]] |> 
  as_tibble() |> 
  dplyr::left_join(mae@colData |> as_tibble()) |> 
  filter(pid == "068100062", .feature == "Lactobacillus_crispatus") |> 
  select(.feature, .sample, visit_code, rel_ab, exclude_sample)

Individual LBP strains by sites

Code 
LBP_strains <- 
  mae[["mg"]] |> 
  as_tibble() |> 
  filter(!is.na(LBP)) |> 
  select(.feature, .sample, uid, rel_ab, LBP, strain_origin, poor_quality_mg_data) |> 
  left_join(mae |> colData() |> as_tibble()) |> 
  filter(mITT, !poor_quality_mg_data, randomized)
Code 
LBP_strains |> 
   mutate(
    visit_label = ifelse(visit != "screening", str_c(visit, " (W", study_week, ")"), visit |> as.character()) |> fct_reorder(visit |> as.numeric()),
    ) |> 
  filter(!is.na(arm), !is.na(study_week), study_week >= 2) |> 
  ggplot() +
  aes(x = .feature, y = rel_ab, col = site, fill = site) +
  # geom_point(alpha = 0.5, size = 0.5, position = position_dodge(width = 0.5)) +
  # geom_violin(col = NA, alpha = 0.4) + # > TODO: check if it looks good - IT DOES NOT!
  ggbeeswarm::geom_quasirandom(alpha = 0.5, size = 0.5, dodge.width = 0.5) +
  # geom_boxplot(outlier.size = 0.5) +
  facet_grid(visit_label ~ LBP + strain_origin , scales = "free_x", space = "free_x") +
  xlab("LBP strain") +
  scale_y_continuous(
    "Relative abundance", labels = scales::percent,
    breaks = seq(0, 1, by = 0.2)
  ) +
  scale_color_manual("Study\nsite", values = site_colors) +
  scale_fill_manual("Study\nsite", values = site_colors) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Code 
LBP_strains |> 
  filter(!is.na(arm), !is.na(study_week), study_week >= 2) |> 
  ggplot() +
  aes(x = .feature, y = rel_ab, col = site, fill = site) +
  # geom_point(alpha = 0.5, size = 0.5) +
  geom_boxplot(outlier.size = 0.5, alpha = 0.4) +
  facet_grid(study_week ~ LBP + strain_origin , scales = "free_x", space = "free_x") +
  xlab("LBP strain") +
  scale_y_continuous(
    "Relative abundance bacteria", labels = scales::percent,
    breaks = seq(0, 1, by = 0.2)
  ) +
  scale_color_manual(values = site_colors) +
  scale_fill_manual(values = site_colors) +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
  )

Code 
LBP_strains |> 
  filter(!is.na(arm), !is.na(study_week), study_week >= 2) |> 
  ggplot() +
  aes(x = study_week, y = rel_ab, col = strain_origin) +
  geom_point(alpha = 0.5, size = 0.5) +
  geom_line(aes(group = pid), alpha = 0.5) +
  facet_grid(site ~ LBP |> str_replace("&", "&\n") + strain_origin + .feature , scales = "free_x", space = "free_x") +
  xlab("Study week") +
  scale_y_continuous(
    "Relative abundance bacteria", labels = scales::percent,
    breaks = seq(0, 1, by = 0.2)
  ) +
  #scale_color_manual(values = colorspace::darken(scale_colour_hue()$palette(2), amount = 0.4)) +
  scale_color_manual("Strain origin", values = strain_origin_colors) +
  scale_x_continuous("Study weeks", breaks = c(0:5,7,9,12), minor_breaks = 0:12) +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
  )

Proportion colonized among exposed

Code 
tmp <- 
  # we use the metagenomics data for this
  mae[["mg"]] |> 
  as_tibble() |> 
  filter(!is.na(LBP), !poor_quality_mg_data) |> 
  select(.feature, LBP, strain_origin, .sample, uid, rel_ab) |> 
  # add the arm, mITT, etc.
  left_join(
    mae |> colData() |> as_tibble() |> select(uid, pid, site, randomized, mITT, arm, visit_code, visit, PIPV), 
    by = join_by(uid)
  ) |>
  # only consider the mITT
  filter(randomized, mITT) |> 
  filter(PIPV, as.numeric(visit_code) >= 1200) |> # only include visits after the first 2 weeks
  # define the outcome of interest for each person and strain
  group_by(.feature, LBP, strain_origin, pid, arm, site) |>
  summarize(
    ever_colonized = any(rel_ab >= 0.05, na.rm = TRUE),
    .groups = "drop"
  ) |> 
  # exclude Placebo participants because, they were, in theory, not exposed to any of the strains.
  filter(arm != "Pl") |> 
  # define the total number of participants exposed for each strain.
  # For LC-115 strains, this is only participants of arm LC-115
  # For LC-106 strains, this is participants from all active arms.
  mutate(
    exposed = case_when(
      LBP == "LC-115" ~ (arm == "LC-115"),
      LBP == "LC-106 & LC-115" ~ TRUE,
      TRUE ~ FALSE
    )
  ) |> 
  # we compute the statistics of interest
  group_by(.feature, LBP, strain_origin, site) |> 
  summarize(
    n_exposed = sum(exposed, na.rm = TRUE),
    n_colonized = sum(ever_colonized & exposed, na.rm = TRUE),
    .groups = "drop"
  ) |> 
  mutate(
    prop_colonized = n_colonized / n_exposed,
    CI_low = binom::binom.confint(n_colonized, n_exposed, method = "wilson")$lower,
    CI_high = binom::binom.confint(n_colonized, n_exposed, method = "wilson")$upper
  )
Code 
tmp |> 
  ggplot() +
  aes(x = .feature, y = prop_colonized, col = site) +
  facet_grid(. ~ LBP + strain_origin, scales = "free", space = "free") +
  geom_errorbar(
    aes(ymin = CI_low, ymax = CI_high), 
    linewidth = 1, alpha = 0.7,
    width = 0.3, position = position_dodge(width = 0.5)
    ) +
  geom_point(position = position_dodge(width = 0.5), size = 2) +
  scale_color_manual("Study site", values = site_colors) +
  xlab("LBP strain") + 
  ylab("Proportion of participants colonized\namong those exposed") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Code 
tmp |> 
  mutate(
    CI = str_c(
      "[", 
      scales::percent(CI_low, accuracy = 1), 
      " - ", 
      scales::percent(CI_high, accuracy = 1), 
      "]"
    ),
    value = 
      str_c(
        scales::percent(prop_colonized, accuracy = 1), 
        " (", n_colonized, "/", n_exposed,")<br>",
       CI
      ),
    strain_info = str_c(LBP, "<br>(", strain_origin, " strains)")
  ) |> 
  select(-n_exposed, -n_colonized, -prop_colonized, -CI_low, -CI_high, -CI, -LBP, -strain_origin) |> 
  pivot_wider(
    names_from = c(site), 
    values_from = value
  ) |>
  arrange(strain_info) |> 
  group_by(strain_info) |> 
  gt(
    row_group_as_column = TRUE, 
    process_md = TRUE,
    caption = "Proportion of mITT participants exposed to a given strain who colonized with that strain (colonization defined as a ≥ 5% relative abundance by metagenomics) at any weekly visit after product exposure."
  ) |> 
  cols_label(.feature = "LBP strain") |> 
  cols_align(columns = .feature, align = "left") |> 
  cols_align(columns = c("CAP", "MGH"), align = "center") |>
  fmt_markdown(columns = everything())
Proportion of mITT participants exposed to a given strain who colonized with that strain (colonization defined as a ≥ 5% relative abundance by metagenomics) at any weekly visit after product exposure.
LBP strain CAP MGH
LC-106 & LC-115
(SA strains)		 FF00018 7% (4/56)
[3% - 17%]		 20% (3/15)
[7% - 45%]
FF00051 30% (17/56)
[20% - 43%]		 67% (10/15)
[42% - 85%]
UC101 25% (14/56)
[16% - 38%]		 73% (11/15)
[48% - 89%]
LC-106 & LC-115
(US strains)		 C0022A1 25% (14/56)
[16% - 38%]		 47% (7/15)
[25% - 70%]
C0059E1 61% (34/56)
[48% - 72%]		 87% (13/15)
[62% - 96%]
C0175A1 39% (22/56)
[28% - 52%]		 67% (10/15)
[42% - 85%]
LC-115
(SA strains)		 122010 57% (8/14)
[33% - 79%]		 33% (2/6)
[10% - 70%]
185329 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
FF00004 50% (7/14)
[27% - 73%]		 83% (5/6)
[44% - 97%]
FF00064 50% (7/14)
[27% - 73%]		 83% (5/6)
[44% - 97%]
FF00072 7% (1/14)
[1% - 31%]		 50% (3/6)
[19% - 81%]
UC119 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
LC-115
(US strains)		 C0006A1 50% (7/14)
[27% - 73%]		 67% (4/6)
[30% - 90%]
C0028A1 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
C0112A1 64% (9/14)
[39% - 84%]		 83% (5/6)
[44% - 97%]

Excluding immediate post-product visit

Code 
tmp <- 
  # we use the metagenomics data for this
  mae[["mg"]] |> 
  as_tibble() |> 
  filter(!is.na(LBP), !poor_quality_mg_data) |> 
  select(.feature, LBP, strain_origin, .sample, uid, rel_ab) |> 
  # add the arm, mITT, etc.
  left_join(
    mae |> colData() |> as_tibble() |> 
      select(uid, pid, site, randomized, mITT, arm, visit_code, visit, PIPV), 
    by = join_by(uid)
  ) |>
  # only consider the mITT
  filter(randomized, mITT) |> 
  mutate(
    include_visit = 
      case_when(
        (arm %in% c("LC-106-7", "LC-115")) & as.numeric(visit_code) > 1200 ~ TRUE,
        (arm %in% c("LC-106-3", "LC-106-o")) & as.numeric(visit_code) >= 1200 ~TRUE,
        TRUE ~ FALSE
      )
  ) |> 
  filter(PIPV, include_visit) |> # only include visits after the first 2 weeks
  # define the outcome of interest for each person and strain
  group_by(.feature, LBP, strain_origin, pid, arm, site) |>
  summarize(
    ever_colonized = any(rel_ab >= 0.05, na.rm = TRUE),
    .groups = "drop"
  ) |> 
  # exclude Placebo participants because, they were, in theory, not exposed to any of the strains.
  filter(arm != "Pl") |> 
  # define the total number of participants exposed for each strain.
  # For LC-115 strains, this is only participants of arm LC-115
  # For LC-106 strains, this is participants from all active arms.
  mutate(
    exposed = case_when(
      LBP == "LC-115" ~ (arm == "LC-115"),
      LBP == "LC-106 & LC-115" ~ TRUE,
      TRUE ~ FALSE
    )
  ) |> 
  # we compute the statistics of interest
  group_by(.feature, LBP, strain_origin, site) |> 
  summarize(
    n_exposed = sum(exposed, na.rm = TRUE),
    n_colonized = sum(ever_colonized & exposed, na.rm = TRUE),
    .groups = "drop"
  ) |> 
  mutate(
    prop_colonized = n_colonized / n_exposed,
    CI_low = binom::binom.confint(n_colonized, n_exposed, method = "wilson")$lower,
    CI_high = binom::binom.confint(n_colonized, n_exposed, method = "wilson")$upper
  )
Code 
tmp |> 
  ggplot() +
  aes(x = .feature, y = prop_colonized, col = site) +
  facet_grid(. ~ LBP + strain_origin, scales = "free", space = "free") +
  geom_errorbar(
    aes(ymin = CI_low, ymax = CI_high), 
    linewidth = 1, alpha = 0.7,
    width = 0.3, position = position_dodge(width = 0.5)
    ) +
  geom_point(position = position_dodge(width = 0.5), size = 2) +
  scale_color_manual("Study site", values = site_colors) +
  xlab("LBP strain") + 
  ylab("Proportion of participants colonized\namong those exposed") +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Code 
tmp |> 
  mutate(
    CI = str_c(
      "[", 
      scales::percent(CI_low, accuracy = 1), 
      " - ", 
      scales::percent(CI_high, accuracy = 1), 
      "]"
    ),
    value = 
      str_c(
        scales::percent(prop_colonized, accuracy = 1), 
        " (", n_colonized, "/", n_exposed,")<br>",
       CI
      ),
    strain_info = str_c(LBP, "<br>(", strain_origin, " strains)")
  ) |> 
  select(-n_exposed, -n_colonized, -prop_colonized, -CI_low, -CI_high, -CI, -LBP, -strain_origin) |> 
  pivot_wider(
    names_from = c(site), 
    values_from = value
  ) |>
  arrange(strain_info) |> 
  group_by(strain_info) |> 
  gt(
    row_group_as_column = TRUE, 
    process_md = TRUE,
    caption = "Proportion of mITT participants exposed to a given strain who colonized with that strain (colonization defined as a ≥ 5% relative abundance by metagenomics) at any weekly visit after product exposure but excluding immediate post-product visit (i.e., excluding week 2 visit for the LC-106-7 and LC-115 arms)."
  ) |> 
  cols_label(.feature = "LBP strain") |> 
  cols_align(columns = .feature, align = "left") |> 
  cols_align(columns = c("CAP", "MGH"), align = "center") |>
  fmt_markdown(columns = everything())
Proportion of mITT participants exposed to a given strain who colonized with that strain (colonization defined as a ≥ 5% relative abundance by metagenomics) at any weekly visit after product exposure but excluding immediate post-product visit (i.e., excluding week 2 visit for the LC-106-7 and LC-115 arms).
LBP strain CAP MGH
LC-106 & LC-115
(SA strains)		 FF00018 2% (1/56)
[0% - 9%]		 7% (1/15)
[1% - 30%]
FF00051 9% (5/56)
[4% - 19%]		 20% (3/15)
[7% - 45%]
UC101 2% (1/56)
[0% - 9%]		 7% (1/15)
[1% - 30%]
LC-106 & LC-115
(US strains)		 C0022A1 21% (12/56)
[13% - 34%]		 33% (5/15)
[15% - 58%]
C0059E1 43% (24/56)
[31% - 56%]		 40% (6/15)
[20% - 64%]
C0175A1 39% (22/56)
[28% - 52%]		 67% (10/15)
[42% - 85%]
LC-115
(SA strains)		 122010 36% (5/14)
[16% - 61%]		 17% (1/6)
[3% - 56%]
185329 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
FF00004 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
FF00064 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
FF00072 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
UC119 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
LC-115
(US strains)		 C0006A1 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
C0028A1 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]
C0112A1 0% (0/14)
[0% - 22%]		 0% (0/6)
[0% - 39%]

Supplementary analyses

TODO

Applicator staining

Numbers

  • Proportion of participants that returned applicators
  • Proportion of participants with returned applicators for which the staining analysis was done

Staining analysis

mITT

Concordance between self-reports and applicator staining:

  • scatter plot of # of self-reported doses vs # of positive staining

  • Number of participants that self-reported using at least one dose (= XX)

  • Number of participants that had at least one positive staining (= YY)

  • contingency table of self-reported doses and positive staining

  • Proportion of participants self-reported using at least one dose and have at least one positive staining (= ZZ/XX)

PP

Same but with at least 6 doses

Time since last dose in 7-days active arms (LC-106-7, LC-115)

We compute this time based on self-reported time of last study product use (daily - CRF plate 32/33) -> date/time of last study product = use the crf 32/33 visit_day + answer to when? (early morning, etc.) -> swab time = use study_day of the weekly visit.

LBP colonization in participants with BV post-MTZ

see “After oral metronidazole treatment, 22/70 (30%) people still had a Nugent score of 7 or higher, indicating a failure to resolve BV. Among the 16 of these people exposed to LBP, ZZ had a detectable LBP strain identified after treatment by metagenomics, and TT by qPCR during treatment.”